Empowering Mizoram State Museum Applying Virtual Reality on Linked Data and Smart Objects
Year: 2025
Type: article
Abstract: -Augmented Reality (AR) and Virtual Reality (VR) can be combined to create Mixed Reality (MR) experiences that significantly benefit India's tourism department. These technologies enhance higher-order cognitive learning by immersing users in realistic environments, making them invaluable complements to traditional museum techniques. This Full Research explores the implementation of various algorithms, including Rendering Algorithm, pathfinding navigation meshes Algorithm, convolutional neural networks Algorithm (CNNs) and Simultaneous Localization and Mapping Algorithm (SLAM), to develop a 3D model of Advanced Mizoram State Museum Environment in Unity Hub. This model was then converted into a VR experience for the Meta Quest 2. First, I go to Mizoram State Museum to Collect the information and take the picture for Creating Dataset and Converting the 3D Image to the Unity Hub Application for advanced Museum Environment Creating Purpose. This Paper Only Explain the two Algorithms only Rendering Algorithm and Convolutional Neural Network Algorithm (CNN). The rendering algorithm was employed to optimize the Animals, Birds, Insects and Leaf's to get the information and Create the Dataset to upload the code. The Unity Hub application wants to them Object Assets. I go to Download to the Various Assets stores Example Unity Hub Assets Store, Sketch Fab and Blender Assets Store Websites. The Convolutional Neural Network Algorithm (CNN) have explained the Object Detection, Object Recognition and Object Identification to the information for the Visitors peoples. The 3D model of Mizoram State Museum effectively showcases biological animals, plants, insects, and human features, offering users the convenience of exploring the museum anytime and anywhere.
Source: Science & Technology Journal
Authors: V.D. Ambeth Kumar, Rajesh Ramasamy, Gurram Ramakrishna, R. Ramesh Prabhakaran
Institution: Mizoram University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407767114